scholarly journals Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Mohammed Rakeibul Hasan ◽  
Mohammed Ishraaf Fatemi ◽  
Mohammad Monirujjaman Khan ◽  
Manjit Kaur ◽  
Atef Zaguia
Keyword(s):  
Skin Cancer ◽  
Mortality Rate ◽  
Support Vector ◽  
Work Processes ◽  
Abnormal Growth ◽  
Skin Cells ◽  
Skin Cancers ◽  
Malignant Skin ◽  
Common Skin ◽  
Cancer Spread

We live in a world where people are suffering from many diseases. Cancer is the most threatening of them all. Among all the variants of cancer, skin cancer is spreading rapidly. It happens because of the abnormal growth of skin cells. The increase in ultraviolet radiation on the Earth’s surface is also helping skin cancer spread in every corner of the world. Benign and malignant types are the most common skin cancers people suffer from. People go through expensive and time-consuming treatments to cure skin cancer but yet fail to lower the mortality rate. To reduce the mortality rate, early detection of skin cancer in its incipient phase is helpful. In today’s world, deep learning is being used to detect diseases. The convolutional neural network (CNN) helps to find skin cancer through image classification more accurately. This research contains information about many CNN models and a comparison of their working processes for finding the best results. Pretrained models like VGG16, Support Vector Machine (SVM), ResNet50, and self-built models (sequential) are used to analyze the process of CNN models. These models work differently as there are variations in their layer numbers. Depending on their layers and work processes, some models work better than others. An image dataset of benign and malignant data has been taken from Kaggle. In this dataset, there are 6594 images of benign and malignant skin cancer. Using different approaches, we have gained accurate results for VGG16 (93.18%), SVM (83.48%), ResNet50 (84.39%), Sequential_Model_1 (74.24%), Sequential_Model_2 (77.00%), and Sequential_Model_3 (84.09%). This research compares these outcomes based on the model’s work process. Our comparison includes model layer numbers, working process, and precision. The VGG16 model has given us the highest accuracy of 93.18%.

ANDROID APPLICATION FOR DETECTION OF SKIN CANCER USING EXPERT SYSTEM

2018 ◽  
Vol 2 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yogi Udjaja
Keyword(s):  
Risk Factor ◽  
Expert System ◽  
Skin Cancer ◽  
Cancer Cells ◽  
System Application ◽  
Ultraviolet Rays ◽  
The Sun ◽  
Android Application ◽  
Abnormal Growth ◽  
Skin Cells

Skin cancer is an abnormal growth of human skin that can damage skin cells. By knowing the symptoms of skin cancer as early as posssible, it is helpful in preventing the spread of cancer cells and treatment. There are many factors that cause skin cancer, but in general the cause is exposure to ultraviolet rays from the sun.  Therefore, an expert system application is required to detect skin cancer.  By using backward chining and probalility methode; which data is in form of someone’s risk factor and clinical sympthoms, then the application will provide temporary diagnosis.  This application is built in an android platfrom because in its development android is more used than other platforms. The accuracy obtained from this application is 96.67%.

scholarly journals Expression profiles of proton-sensing G-protein coupled receptors in common skin tumors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wybke Klatt ◽  
Susanne Wallner ◽  
Christoph Brochhausen ◽  
Judith A. Stolwijk ◽  
Stephan Schreml
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Skin Tumors ◽  
Solid Cancer ◽  
Tissue Samples ◽  
Skin Cancers ◽  
Common Skin

Abstract The proton-sensing GPCRs (pH-GPCRs) GPR4 (GPR19), TDAG8 (GPR65, T-cell death associated gene 8), OGR1 (GPR68, ovarian cancer GPCR1), and G2A (GPR132, G2 accumulation protein) are involved in sensing and transducing changes in extracellular pH (pHe). Extracellular acidification is a central hallmark of solid cancer. pH-GPCR function has been associated with cancer cell proliferation, adhesion, migration and metastasis, as well as with modulation of the immune system. Little is known about the expression levels and role of pH-GPCRs in skin cancer. To better understand the functions of pH-GPCRs in skin cancer in vivo, we examined the expression-profiles of GPR4, TDAG8, OGR1 and G2A in four common skin tumors, i.e. squamous cell carcinoma (SCC), malignant melanoma (MM), compound nevus cell nevi (NCN), basal cell carcinoma (BCC). We performed immunohistochemistry and immunofluorescence staining on paraffin-embedded tissue samples acquired from patients suffering from SCC, MM, NCN or BCC. We show the expression of pH-GPCRs in four common skin cancers. Different expression patterns in the investigated skin cancer types indicate that the different pH-GPCRs may have distinct functions in tumor progression and serve as novel therapeutic targets.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals A rare case of intracranial malignant melanoma with an unusual presentation

2016 ◽  
Vol 7 (6) ◽  
pp. 91-93
Author(s):  
Sandeep B V ◽  
Suniti Kumar Saha ◽  
Manpreet Singh Banga ◽  
Partha Ghosh
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Head And Neck ◽  
Rare Case ◽  
Medical Sciences ◽  
Skin Cancers ◽  
Common Skin ◽  
The Common ◽  
Sixth Decade ◽  
Geographical Locations

Among the common skin cancers, melanoma is the most lethal. Although, it comprises only 3% of all skin cancers diagnosed , it accounts for about  75% of all skin cancer-related deaths. Melanoma is a relatively uncommon skin cancer in geographical locations like India. Its highest incidence is seen in sixth decade . Head and neck melanomas constitute approximately 17% of all cutaneous melanomas .We present a 15 year old male patient who presented with a intracranial melanoma with osteolytic skull lesion.Asian Journal of Medical Sciences Vol.7(5) 2016 91-93

scholarly journals Skin Cancer Recognition Using SVM Image Processing Technique

2020 ◽  
pp. 11-15
Author(s):  
Rahul Chand Thakur ◽  
◽  
Vaibhav Panwar ◽  
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Supervised Learning ◽  
Detection System ◽  
Early Stage ◽  
Image Features ◽  
The Body ◽  
Image Processing Technique ◽  
Support Vector ◽  
Skin Cells

Skin cancer is considered as commonest cause of death among humans in today's world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient's life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure [1] is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It's a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machine (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several preprocessing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not.

scholarly journals Regulation of Apoptosis during Environmental Skin Tumor Initiation

2021 ◽  
Author(s):  
Bilal Bin Hafeez ◽  
Eunmi Park ◽  
Kyung-Soo Chun ◽  
Yong-Yeon Cho ◽  
Dae Joon Kim
Keyword(s):  
Skin Cancer ◽  
Signaling Pathways ◽  
The United States ◽  
Common Type ◽  
Skin Tumor ◽  
Tumor Initiation ◽  
Environmental Carcinogens ◽  
Skin Cells ◽  
Skin Cancers ◽  
The Impact

Skin cancer is more prevalent than any other cancer in the United States. Non-melanoma skin cancers are the more common forms of skin cancer that affect individuals. The development of squamous cell carcinoma, the second most common type of skin cancer, can be stimulated by exposure of environmental carcinogens, such as chemical toxicants or UVB. It is developed by three distinct stages: initiation, promotion, and progression. During the initiation, the fate of DNA-damaged skin cells is determined by the homeostatic regulation of pro-apoptotic and anti-apoptotic signaling pathways. The imbalance or disruption of either signaling will lead to the survival of initiated cells, resulting in the development of skin cancer. In this chapter, we will discuss signaling pathways that regulate apoptosis and the impact of their dysfunction during skin tumor initiation.

scholarly journals Difficulties of diagnosis and treatment in a case of malignant melanoma

2015 ◽  
Vol 62 (3) ◽  
pp. 307-312
Author(s):  
Marieta Petrescu ◽  
◽  
Adrian Alexandru ◽  
Andreea Benga ◽  
Doina Dumitrescu ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Mortality Rate ◽  
Stage Iv ◽  
Primary Diagnosis ◽  
Rare Form ◽  
Biological Condition ◽  
Skin Cancers ◽  
Melanoma Patients ◽  
Therapeutic Possibilities

Malignant melanoma is a rare form of skin cancer, which is only about 3-4% of all skin cancers, but results in a mortality rate up to 65-70%. The vast majority of melanomas are diagnosed in stage 0, I and II, but there were cases when the primary diagnosis of malignant melanoma patients was put directly in stage IV metastatic disseminated lymph nodes, lung, liver, spleen, bone, brain. In the case of a patient with metastatic melanoma, poor biological condition limits the diagnostic and therapeutic possibilities.

scholarly journals A Hybrid High Performance Intelligent Computing Approach of CACNN and RNN for Skin Cancer Image Grading

2021 ◽  
Author(s):  
Manimurugan S
Keyword(s):  
Skin Cancer ◽  
High Performance ◽  
Spatial Arrangement ◽  
The Cancer Genome Atlas ◽  
Intelligent Computing ◽  
Skin Cells ◽  
Skin Cancers ◽  
Cancer Genome Atlas ◽  
Histological Images ◽  
Computational Pathology

Abstract Skin cancer is characterized as the uncontrollable growth of skin cells caused by unrepairable DNA damage. Melanoma is the deadliest form of skin cancers caused by melanocyte and early diagnosis supports therapists in curing it. Computational pathology offers a one-of-a-kind ability to spatially dissect certain interfaces on digitized histology images. A hybrid context-aware convolutional neural networks with recurrent neural network (CA-CNN-RNN) based on skin cancer histological images is proposed in this research. The proposed model encodes a histology image's local representation into higher-dimensional features first, then aggregated the feature by consider their spatial arrangement to enable the final predictions. In this research, H&E-stained sectioned images from the Cancer Genome Atlas are used as the dataset for assessment. From 58 images, 37 images were used for training and 21 images are used for testing. The process on histology images of melanoma skin cancer was analyzed and validated with various classifiers such as VGG-19, Inception, ResNet50, and DarkNet-53 using the hybrid CA-CNN-RNN model. The dataset is used to generate the results, which are then analyzed based on criteria such as accuracy, recall, precision, and F-score. The performance analysis shows that the proposed CA-CNN-RNN with different classifiers has performed better and among the classifiers the DarkNet-53 model has the better performance in all the parameters.

scholarly journals Multiple skin neoplasms in subjects under 40 years of age in Goiania, Brazil

2015 ◽  
Vol 49 (0) ◽  
Author(s):  
Samir Pereira ◽  
Maria Paula Curado ◽  
Ana Maria Quinteiro Ribeiro
Keyword(s):  
Skin Cancer ◽  
Ultraviolet Radiation ◽  
Skin Diseases ◽  
Skin Neoplasms ◽  
Population Based ◽  
Annual Percentage Change ◽  
Multiple Lesions ◽  
Skin Cancers ◽  
Malignant Skin ◽  
Nonmelanoma Skin Cancers

OBJECTIVE To describe the trend for malignant skin neoplasms in subjects under 40 years of age in a region with high ultraviolet radiation indices.METHODS A descriptive epidemiological study on melanoma and nonmelanoma skin cancers that was conducted in Goiania, Midwest Brazil, with 1,688 people under 40 years of age, between 1988 and 2009. Cases were obtained fromRegistro de Câncer de Base Populacional de Goiânia(Goiania’s Population-Based Cancer File). Frequency, trends, and incidence of cases with single and multiple lesions were analyzed; transplants and genetic skin diseases were found in cases with multiple lesions.RESULTS Over the period, 1,995 skin cancer cases were observed to found, of which 1,524 (90.3%) cases had single lesions and 164 (9.7%) had multiple lesions. Regarding single lesions, incidence on men was observed to have risen from 2.4 to 3.1/100,000 inhabitants; it differed significantly for women, shifting from 2.3 to 5.3/100,000 (Annual percentage change – [APC] 3.0%, p = 0.006). Regarding multiple lesions, incidence on men was observed to have risen from 0.30 to 0.98/100,000 inhabitants; for women, it rose from 0.43 to 1.16/100,000 (APC 8.6%, p = 0.003). Genetic skin diseases or transplants were found to have been correlated with 10.0% of cases with multiple lesions – an average of 5.1 lesions per patient. The average was 2.5 in cases without that correlation.CONCLUSIONS Skin cancer on women under 40 years of age has been observed to be increasing for both cases with single and multiple lesions. It is not unusual to find multiple tumors in young people – in most cases, they are not associated with genetic skin diseases or transplants. It is necessary to avoid excessive exposure to ultraviolet radiation from childhood.

The potential cutaneous effects of stratospheric ozone depletion

1996 ◽  
Vol 4 (1) ◽  
pp. 1-7
Author(s):  
John H. Epstein
Keyword(s):  
Skin Cancer ◽  
Lupus Erythematosus ◽  
Skin Diseases ◽  
Stratospheric Ozone ◽  
Ultraviolet B ◽  
Uvb Radiation ◽  
Skin Cancers ◽  
Light Skin ◽  
Solar Urticaria ◽  
Common Skin

Recent evidence indicates that there has been a reduction in the stratospheric ozone over the northern hemisphere, as well as the Antarctic and Arctic latitudes. This has resulted in an increased penetration of ultraviolet B (UVB) at least as measured at Toronto, Canada, since 1989. If no precautions are observed by the human population, this could eventually result in an increase in the skin cancer incidence. This would be especially true for the most common cancers, that is, the nonmelanoma skin cancers (NMSCs), basal cell carcinomas and squamous cell carcinomas. In addition it has been predicted that the third most common skin cancer, the malignant melanoma, would also increase in incidence. However, the relationship between UVB radiation and melanoma formation is much less clear than it is for NMSCs. Clinically people with a loss or lack of melanin protection such as those with occulocutaneous albinism and vitiligo, or much more commonly, people with light skin, eyes, and hair would be at greatest risk. Also increased UVB penetration could exacerbate certain infections such as herpes simplex. People with UVB-sensitive diseases including solar urticaria, polymorphous light eruptions, lupus erythematosus, dermatomyositis, pemphigus, pemphigoid, Darier's disease, familial benign chronic pemphigus, and certain recessive degenerative genodermatoses would also be potentially more vulnerable.Key words: ozone, ultraviolet B (UVB), skin cancer, photosensitive skin diseases.

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